The present invention relates to optimizing fuzzy logic controllers used in rotating induction machine systems.
A rotating induction machine system transfers power between a stationary member, the stator, and a rotating member, the rotor, by electromagnetic induction. The stator contains one or more sequences of coils placed in parallel slots along the stator length. The rotor is designed to revolve within the stator, typically carrying electromagnets driven by an excitation or flux control current. An induction machine may be operated as a motor by supplying alternating current electrical power to the stator. The stator current produces rotating magnetic fields within the stator, causing the rotor to revolve, and producing output power as torque at some rotational velocity. An induction machine may also be operated as a generator by suppling rotational power to the rotor, thereby inducing alternating electrical power in the stator windings.
In addition to the machine itself, additional components are typically required to drive and control the induction machine. An inverter or rectifier-inverter combination is used to provide cyclic current for motor speed control. A velocity controller compensates for slip between the rotating magnetic field and the rotor. A vector current controller determines the magnitude and timing of current control pulses.
Fuzzy logic control has been introduced into induction machine systems for estimating control parameters and for generating control signals. A fuzzy logic controller assigns each input sample to one or more sets of a membership function. Inference rules are then used to generate output values based on the membership sets of the input variables. The outputs are then xe2x80x9cdefuzzifiedxe2x80x9d to generate control output signals. Fuzzy decision parameters, such as membership functions, must be optimized or tuned to achieve desired control characteristics.
An example application of a fuzzy logic controller is in controlling an induction starter/alternator for a hybrid electric vehicle. One such controlled device is the starter/alternator described in a paper by J. M. Miller et al. titled xe2x80x9cStarter-Alternator for Hybrid Electric Vehicle: Comparison of Induction and Variable Reluctance Machines and Drives,xe2x80x9d appearing in IEEE IAS Conference Proceedings, pp. 513-523, 1998, which is incorporated by reference herein.
The induction starter/alternator may be controlled by both a velocity controller for maintaining constant rotational velocity and by a fuzzy logic controller for optimizing power efficiency. Such control systems are described in a paper by G.C.D. Sousa et al. titled xe2x80x9cFuzzy Logic Based On-Line Efficiency Optimization Control of an Indirect Vector Controlled Induction Motor Drive,xe2x80x9d appearing in IEEE-IECON Conference Record, pp. 1168-1174, 1993, and in U.S. Pat. No. 5,652,485 titled xe2x80x9cFuzzy Logic Integrated Electrical Control To Improve Variable Speed Wind Turbine Efficiency And Performance,xe2x80x9d issued Jul. 29, 1997, to Spiegel et al., each of which is incorporated by reference herein.
What is needed is improved optimization of fuzzy logic motor controller operating parameters. The optimization method should easily adapt to any changes made to the controlled system. Further, the optimization method should permit automatic tuning of fuzzy logic control parameters.
The present invention optimizes parameters of a fuzzy logic controller through simulation without having to develop complex and potentially inaccurate models of the controlled rotating induction machine system.
In general, the fuzzy logic controller has at least one input and at least one output, each input accepting a machine system operating parameter and each output producing at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. The goal is to optimize the fuzzy logic decision parameters. To begin optimization, a set of data relating control parameters to operating parameters are obtained for each of one or more machine operating regions. A measurement-based model is constructed for each machine operating region based on the obtained data. The fuzzy logic controller is simulated with at least one created model in a feedback loop from at least one fuzzy logic output to at least one fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation results. Simulating with a measurement-based model of the machine system is known as hardware-data-in-the-simulation-loop (HDSL). The use of HDSL eliminates the need to develop mathematical models representing the machine system. Such mathematical models are often imprecise approximations of the actual system. Also, any changes in the machine system requires reformulating the model.
Typically, fuzzy logic decision parameters include fuzzy membership function ranges. These values are adjusted to obtain a desired dynamic response for the machine system to a disturbance input or to a change in desired operating region. Simulating a change in desired operating region may be accomplished by switching between models based on different machine operating regions during the simulation. The fuzzy logic controller input is then monitored to determine if the response has the desired dynamic characteristics, such as settling time, overshoot, and the like. If the response does not meet dynamic requirements, membership ranges may be varied.
Decision parameters may be changed manually. As an alternative to manually changing fuzzy logic decision parameters and running the simulation again, decision parameters can be automatically tuned using adaptive neuro-fuzzy inference provided by an adaptive neuro-fuzzy inference system (ANFIS).
A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
In embodiments of the present invention, operating parameters may include a measure of machine system output power and machine flux current.
In another embodiment of the present invention, a plurality of models are created. Simulating the fuzzy logic controller includes switching between models to create a disturbance input to the fuzzy logic controller.
In yet another embodiment of the present invention, fuzzy logic decision parameters include fuzzy membership functions.
In still other embodiments of the present invention, fuzzy logic decision parameters are optimized to achieve a desired dynamic response for the machine system or to optimize machine system power efficiency.
In a further embodiment of the present invention, fuzzy logic decision parameters are optimized using adaptive neuro-fuzzy inference.
In a still further embodiment of the present invention, constructing each model includes building a table for each machine operating region relating at least one machine system operating parameter to at least one machine system control parameter.
A method for optimizing a fuzzy controller for a starter/alternator system is also provided. The fuzzy logic controller has an input for system power and an output for flux current control. The fuzzy logic controller generates current control output based on power input and on fuzzy logic decision parameters. Optimizing the fuzzy logic controller includes obtaining a set of data relating the system power to the current control for at least one machine operating region. A model is constructed for each machine operating region based on the obtained data. The fuzzy logic controller is simulated with at least one created model in a feedback loop from the current control output to the power input. Fuzzy logic decision parameters are optimized based on the simulation.